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Record W1963554996 · doi:10.1089/fpd.2009.0474

Intestinal Mucosa Adherence and Cytotoxicity of a Sorbitol-Fermenting, Shiga-Toxin-Negative <i>Escherichia coli</i> O157:NM Isolate with an Atypical Type III Secretion System

2010· article· en· W1963554996 on OpenAlexafffund
Brigitte Lefebvre, Moussa S. Diarra, John M. Fairbrother, Éric Nadeau, Maurice Junior Dubois, François Malouin

Bibliographic record

VenueFoodborne Pathogens and Disease · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntiminShiga toxinMicrobiologyBiologyEscherichia coliShiga-like toxinVero cellToxinCytotoxicitySecretionDiarrheaVirologyIn vitroEnterobacteriaceaeGeneVirusBiochemistryMedicine

Abstract

fetched live from OpenAlex

Reports show that sorbitol-fermenting (SF) Escherichia coli O157 isolates are implicated in animal and human diseases and may represent new emerging pathogens. We investigated the cytotoxicity and interaction with intestinal tissues of an SF, Shiga-toxin-negative E. coli O157:NM isolate. This bovine isolate was negative for stx genes and was not cytotoxic for Vero cells. We found that this E. coli O157 isolate possesses an intimin of type beta, whereas the translocated intimin receptor Tir and type III secretion system components EspA, EspB, and EspD were of type alpha. In contrast, Shiga-toxin-positive O157:H7 isolates usually possess variants of type gamma. The isolate did not present typical O157:H7 attaching and effacing lesions in the newborn pig ileal in vitro organ culture model. However, extensive effacement and elongation of the microvilli were observed. In vitro organ culture results suggest that such an SF, Shiga-toxin-negative O157:NM isolate found in cattle may potentially cause disease, such as diarrhea without hemolytic uremic syndrome, in humans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.250
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2010
Admission routes2
Has abstractyes

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